temp
Abhraneel Sarma
2/19/2022
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(ggplot2)
library(purrr)
library(broom)
library(gganimate)
library(cowplot)
devtools::load_all(".")## ℹ Loading multiverse## Loading required package: knitrknit_as_emar()Multiverse case study
data("durante")
data.raw.study2 <- durante %>%
mutate(
Abortion = abs(7 - Abortion) + 1,
StemCell = abs(7 - StemCell) + 1,
Marijuana = abs(7 - Marijuana) + 1,
RichTax = abs(7 - RichTax) + 1,
StLiving = abs(7 - StLiving) + 1,
Profit = abs(7 - Profit) + 1,
FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana,
RelComp = round((Rel1 + Rel2 + Rel3)/3, 2)
)Multiverse Analysis
To implement a multiverse analysis, we first need to create the
js parameter('masfem') object
M = multiverse()df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
dplyr::filter(TRUE)df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
dplyr::filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)df = data.raw.study2 %>%
mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
dplyr::filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)df = data.raw.study2 %>%
mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast ) %>%
dplyr::filter( branch(cycle_length,
"cl_option1" ~ TRUE,
"cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
"cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
))df = df %>%
dplyr::filter(TRUE)df = df %>%
dplyr::filter(Sure1 > 6 | Sure2 > 6)df = df %>%
dplyr::filter(TRUE)df = df %>%
dplyr::filter(Sure1 > 6 | Sure2 > 6)df = df %>%
dplyr::filter(TRUE)df = df %>%
dplyr::filter(Sure1 > 6 | Sure2 > 6)df = df %>%
dplyr::filter( branch(certainty,
"cer_option1" ~ TRUE,
"cer_option2" ~ Sure1 > 6 | Sure2 > 6
))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = StartDateNext) %>%
mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))df = df %>%
mutate(NextMenstrualOnset = branch(menstrual_calculation,
"mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
"mc_option2" %when% (cycle_length != "cl_option2") ~ StartDateofLastPeriod + ReportedCycleLength,
"mc_option3" ~ StartDateNext)
) %>%
mutate(
CycleDay = 28 - (NextMenstrualOnset - DateTesting),
CycleDay = ifelse(WorkerID == 15, 11, ifelse(WorkerID == 16, 18, CycleDay)),
CycleDay = ifelse(CycleDay > 1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28))
)df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
17 & CycleDay <= 27, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
18 & CycleDay <= 25, "low", NA))))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))df = df %>%
mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))df = df %>%
mutate( Fertility = branch( fertile,
"fer_option1" ~ factor( ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 25, "low", NA)) ),
"fer_option2" ~ factor( ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 27, "low", NA)) ),
"fer_option3" ~ factor( ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >= 18 & CycleDay <= 25, "low", NA)) ),
"fer_option4" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low") ),
"fer_option5" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low") )
))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
2, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))df = df %>%
mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
3 | Relationship == 4, "Relationship", NA))))df = df %>%
mutate(RelationshipStatus = branch(relationship_status,
"rs_option1" ~ factor(ifelse(Relationship==1 | Relationship==2, 'Single', 'Relationship')),
"rs_option2" ~ factor(ifelse(Relationship==1, 'Single', 'Relationship')),
"rs_option3" ~ factor(ifelse(Relationship==1, 'Single', ifelse(Relationship==3 | Relationship==4, 'Relationship', NA))) )
)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
broom::tidy(conf.int = TRUE)fit_RelComp <- lm( RelComp ~ Fertility * RelationshipStatus, data = df )
summary_RelComp <- fit_RelComp %>%
broom::tidy( conf.int = TRUE )hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
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hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
hist(summary_RelComp$estimate)
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hist(summary_RelComp$estimate)expand(M)## # A tibble: 210 × 10
## .universe cycle_l…¹ certa…² menst…³ fertile relat…⁴ .parameter…⁵ .code
## <int> <chr> <chr> <chr> <chr> <chr> <list> <list>
## 1 1 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 2 2 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 3 3 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 4 4 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 5 5 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 6 6 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 7 7 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 8 8 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 9 9 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 10 10 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## # … with 200 more rows, 2 more variables: .results <list>, .errors <list>, and
## # abbreviated variable names ¹cycle_length, ²certainty,
## # ³menstrual_calculation, ⁴relationship_status, ⁵.parameter_assignment
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# don't need this when compiling as package compiles everything
# execute_multiverse(M)extract_results_json = function (multiverse, summary_obj, filename) {
if (!is.multiverse(multiverse)) stop(deparse(multiverse), " needs to be an object of class multiverse")
summary_obj = as_name(enquo(summary_obj))
.summary_obj_default = extract_variable_from_universe(multiverse, 1, summary_obj)
if (!tibble::is_tibble(.summary_obj_default)) stop(summary_obj, " declared inside the multiverse analysis needs to be an object of class tibble or data.frame; please create ", summary_obj, " using broom::tidy or an analogous function")
if (!all(c("term", "estimate", "std.error") %in% names(.summary_obj_default))) stop(summary_obj, " declared inside the multiverse analysis needs to contain the following columns: `term`, `estimate` and `std.error`")
## ISSUE: if the summary_obj has a distributional object
expand(multiverse) %>%
extract_variables(!!sym(summary_obj)) %>%
select(-.code, -.results, -.errors) %>%
rename(results = summary_obj) %>%
unnest(results) %>%
mutate(
# do we want to perform any text processing on the output of broom::tidy?
# I think we want the user to do these modifications instead of trying to do this on our own??
# term = ifelse(term == "(Intercept)", 'Intercept', term)
min = estimate - 5*std.error,
max = estimate + 5*std.error
) %>%
group_by(term) %>%
mutate(min = min(min), max = max(max)) %>%
mutate(
cdf.x = pmap(list(min, max, estimate, std.error), ~ seq(..1, ..2, length.out = 101)),
cdf.y = pmap(list(cdf.x, estimate, std.error), ~ pnorm(..1, ..2, ..3))
) %>%
nest(results = c(term:cdf.y)) %>%
jsonlite::write_json(filename, pretty = TRUE)
}
extract_results_json(M, fit.summary, 'data2.json')